File size: 5,128 Bytes
4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 5c3cfae 4db0438 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | """Task generator — produces (TaskSpec, FullLatentState) pairs for episodes.
Supports three modes:
1. Select from the pre-defined scenario library.
2. Randomly perturb a scenario for domain-randomisation.
3. Compose a fully procedural scenario (tissue × modality × difficulty).
"""
from __future__ import annotations
from typing import List, Optional, Tuple
import numpy as np
from models import TaskSpec, tools_for_modality, assays_for_modality
from server.simulator.latent_state import (
CellPopulation,
ExperimentProgress,
FullLatentState,
GeneProgram,
LatentBiologicalState,
ResourceState,
TechnicalState,
)
from .scenarios import SCENARIO_LIBRARY, Scenario
from .procedural_generator import generate_procedural_scenarios
class TaskGenerator:
"""Generates task + latent-state pairs for environment episodes."""
def __init__(
self,
scenarios: Optional[List[Scenario]] = None,
domain_randomise: bool = True,
):
if scenarios is not None:
self.scenarios = scenarios
else:
self.scenarios = list(SCENARIO_LIBRARY) + generate_procedural_scenarios(n=20, seed=42)
self.domain_randomise = domain_randomise
def generate(
self,
*,
seed: Optional[int] = None,
scenario_name: Optional[str] = None,
) -> Tuple[TaskSpec, FullLatentState]:
rng = np.random.default_rng(seed)
if scenario_name:
scenario = self._find_scenario(scenario_name)
else:
idx = int(rng.integers(0, len(self.scenarios)))
scenario = self.scenarios[idx]
task = scenario.task.model_copy(deep=True)
biology = scenario.biology.model_copy(deep=True)
technical = scenario.technical.model_copy(deep=True)
if self.domain_randomise:
self._randomise(rng, task, biology, technical)
# Filter available tools/assays to those compatible with the modality.
compatible_tools = [t.name for t in tools_for_modality(task.modality)]
compatible_assays = [a.name for a in assays_for_modality(task.modality)]
if compatible_tools:
task.available_tools = compatible_tools
if compatible_assays:
task.available_assays = compatible_assays
latent = FullLatentState(
biology=biology,
technical=technical,
progress=ExperimentProgress(),
resources=ResourceState(
budget_total=task.budget_limit,
time_limit_days=task.time_limit_days,
),
hidden_failure_conditions=list(scenario.hidden_failure_conditions),
task_modality=task.modality,
rng_seed=seed or 0,
)
return task, latent
def list_scenarios(self) -> List[str]:
return [s.name for s in self.scenarios]
# ── internals ───────────────────────────────────────────────────────
def _find_scenario(self, name: str) -> Scenario:
for s in self.scenarios:
if s.name == name:
return s
available = ", ".join(self.list_scenarios())
raise ValueError(f"Unknown scenario '{name}'. Available: {available}")
def _randomise(
self,
rng: np.random.Generator,
task: TaskSpec,
bio: LatentBiologicalState,
tech: TechnicalState,
) -> None:
budget_scale = float(rng.uniform(0.7, 1.3))
task.budget_limit *= budget_scale
task.time_limit_days *= float(rng.uniform(0.8, 1.2))
tech.dropout_rate = float(np.clip(
tech.dropout_rate + rng.normal(0, 0.02), 0.01, 0.3
))
tech.doublet_rate = float(np.clip(
tech.doublet_rate + rng.normal(0, 0.01), 0.01, 0.15
))
tech.sample_quality = float(np.clip(
tech.sample_quality + rng.normal(0, 0.05), 0.5, 1.0
))
tech.ambient_rna_fraction = float(np.clip(
tech.ambient_rna_fraction + rng.normal(0, 0.01), 0.01, 0.15
))
for batch_id in list(tech.batch_effects.keys()):
tech.batch_effects[batch_id] = float(np.clip(
tech.batch_effects[batch_id] + rng.normal(0, 0.03), 0.0, 0.4
))
for pop in bio.cell_populations:
pop.proportion = float(np.clip(
pop.proportion * rng.uniform(0.8, 1.2), 0.01, 0.8
))
total = sum(p.proportion for p in bio.cell_populations) or 1.0
for pop in bio.cell_populations:
pop.proportion /= total
for comparison, effects in bio.true_de_genes.items():
for gene in list(effects.keys()):
effects[gene] *= float(rng.uniform(0.8, 1.2))
bio.n_true_cells = max(
1000,
int(bio.n_true_cells * rng.uniform(0.6, 1.4)),
)
|